In plant breeding, conventional selection is based on phenotypic assessments of progeny in breeding populations. Progeny are typically phenotyped during the growing season, and superior individuals are selected based on their phenotypic scores. For the majority of field crops, there is only one growing season per year. Hence, a limitation of phenotypic selection is that it is routinely limited to one cycle within each year. Another frequent shortcoming of phenotypic selection is the influence of environmental noise on phenotypic expression of traits. This environmental noise can lead to biases in selection and decreases in the selection efficiencies of phenotypic selection.
The development of molecular technologies facilitates methods for utilizing molecular markers to speed up selective breeding processes. One such molecular technology is marker-assisted selection (MAS; also referred to as “marker-aided breeding”). With MAS, one first identifies one or more quantitative trait loci (QTLs) associated with a trait of interest, and then employs analyses of these QTLs in subsequent selections (Lande & Thompson, 1990). In general, MAS can be performed over multiple cycles per year, and genetic gain can be enhanced by intensive selections of targeted QTLs.
However, it is often difficult to identify all of the QTLs that are associated with a particular trait of interest, thereby reducing the overall effectiveness of MAS (Utz et al. 1999; Jannink et al., 2010). This is due to missing QTLs with small effects during QTL identification due to various technical reasons (e.g., lower heritability, small sample sizes, etc.). Failure to identify such QTLs can make MAS difficult and/or inefficient to employ for improving important traits such as crop yield. In addition, QTL effects can be overestimated (Beavis, 1994), which further reduces the efficiency of MAS (Jannink et al., 2010).
Genome-wide selection (GWS; Meuwissen et al., 2001) is a technique that has been proposed to address some of the shortcomings of MAS (Bernardo and Yu, 2007; Jannink et al., 2010). The GWS strategy incorporates all genetic markers available for a given genome into a prediction model simultaneously, thereby reducing risks of missing or inaccurately calculating effects of QTLs with minor effects. Each marker is generally considered to be a putative QTL and all markers are combined to predict the genomic breeding values (GBV) of progeny with GWS. Simulations and empirical studies have verified advantages of GWS over MAS and PS (Meuwissen et al., 2001; Bernardo and Yu, 2007; Hayes et al., 2009; Lorenzana & Bernardo, 2009; Luan et al., 2009).
Typically, GWS can be used to select superior progeny based on their own particular GBVs. (Bernardo & Yu, 2007; Jannink et al., 2010). For example, GWS is generally used to estimate the effects of all assayed markers based on a training population, allowing for the calculation of an overall GBV for each progeny based on the progeny's genome. Progeny can then be ranked with respect to GBV, and the top progeny can be promoted to one or more further cycles of breeding & Yu
However, a given selection strategy might not be optimal for GWS. For example, crossing two top lines selected in a given cycle does not necessarily generate highly-performing progeny by breeding
Therefore, new methods are needed that outperform conventional GBV-based MAS in order to maximize the benefits of GWS on selective breeding strategies.